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Alif Silpachai; Reza Neiriz; MacKenzie Novotny; Ricardo Gutierrez-Osuna; John M. Levis; Evgeny Chukharev – Language Learning & Technology, 2024
It is unclear whether corrective feedback (CF) provided by L2 computer-assisted pronunciation training (CAPT) tools must be 100% accurate to promote an acceptable level of improvement in pronunciation. Using a web-based interface, 30 native speakers of Chinese completed a pretest, a computer-based training session to produce nine sound contrasts…
Descriptors: College Students, Foreign Students, English (Second Language), Second Language Instruction
Elsayed Issa; Gus Hahn-Powell – Language Learning & Technology, 2025
This study investigates the effectiveness of a computer-assisted pronunciation training (CAPT) system on second language learners' acquisition of three grammatical features. It presents a CAPT system on top of a phoneme-based, fine-tuned speech recognition model, and is intended to deliver explicit, corrective feedback on the pronunciation of the…
Descriptors: Grammar, Computer Assisted Instruction, Arabic, Second Language Instruction
Siqi Wang; Jian Li; Qian Liang – Language Learning & Technology, 2024
Drawing on skill acquisition theory (DeKeyser, 2017) and the Information Feedforward and Feedback Loop model (de Bot, 1980), this study aimed to explore the effects of digital zoom technology as a visual reinforcement tool (VRT) in foreign language (FL) pronunciation instruction on learners' segmental production, and learners' attitudes toward and…
Descriptors: Visual Aids, Pronunciation Instruction, Second Language Instruction, Spanish
Yucel Yilmaz; Gisela Granena; Laia Canals; Aleksandra Malicka – Language Learning & Technology, 2024
The present study examines the impact of the explicitness of corrective feedback and explicit associative memory on the acquisition of -ing/-ed participial adjectives through delayed video-based corrective feedback. Fifty-two L1 Spanish learners were randomly assigned to one of three groups (implicit, explicit, or no-feedback) and performed an…
Descriptors: Foreign Countries, College Students, English (Second Language), Second Language Instruction
Cámara-Arenas, Enrique; Tejedor-García, Cristian; Tomas-Vázquez, Cecilia Judith; Escudero-Mancebo, David – Language Learning & Technology, 2023
This study addresses the issue of automatic pronunciation assessment (APA) and its contribution to the teaching of second language (L2) pronunciation. Several attempts have been made at designing such systems, and some have proven operationally successful. However, the automatic assessment of the pronunciation of short words in segmental…
Descriptors: English (Second Language), Second Language Learning, Pronunciation, Pronunciation Instruction
Qing Ma; Ming Ming Chiu – Language Learning & Technology, 2024
Students often have difficulties in self-regulating their vocabulary learning in mobile-assisted language learning (MALL). Building on past studies of vocabulary learning, MALL, self-regulation, and personalised learning (PL), we propose a self-regulated, collaborative, personalised vocabulary (SCPV) learning approach in MALL. In this exploratory…
Descriptors: Self Management, Vocabulary Development, Handheld Devices, Telecommunications
Michael Li – Language Learning & Technology, 2023
The acquisition of Chinese characters has been widely acknowledged as challenging for learners of Chinese as a foreign language (CFL) due to their unique logographic nature and the time and effort involved. However, recent advancements in instructional technologies demonstrate a promising role in facilitating the teaching and learning of Chinese…
Descriptors: Orthographic Symbols, Technology Uses in Education, Teaching Methods, Research Reports
Yiran Wen; Jian Li; Hongkang Xu; Hanwen Hu – Language Learning & Technology, 2023
The problem of cognitive overload is particularly pertinent in multimedia L2 classroom corrective feedback (CF), which involves rich communicative tools to help the class to notice the mismatch between the target input and learners' pronunciation. Based on multimedia design principles, this study developed a new multimodal CF model through…
Descriptors: Error Correction, Videoconferencing, Second Language Learning, Second Language Instruction
Hansol Lee; Jang Ho Lee – Language Learning & Technology, 2024
Artificial intelligence (AI) has considerably advanced the methods for individualizing language learning opportunities, such as assessing learning progress and recommending effective individual instruction. In the present study, we conducted a meta-analysis to synthesize recent empirical findings pertaining to the utilization of AI-guided language…
Descriptors: Artificial Intelligence, Teaching Methods, Learning Processes, Computer Software
Emad A. Alghamdi; Paul Gruba; Ahmed Masrai; Eduardo Velloso – Language Learning & Technology, 2023
Although measures of lexical complexity are well established for printed texts, there is currently no equivalent work for videos. This study, therefore, aims to investigate whether existing lexical complexity measures can be extended to predict second language (L2) learners' judgment of video difficulty. Using a corpus of 320 instructional videos,…
Descriptors: Difficulty Level, Interactive Video, Computer Assisted Instruction, Word Frequency
Robert Godwin-Jones – Language Learning & Technology, 2025
Less commonly taught languages (LCTLs) have traditionally lagged behind in terms of the availability of learning/teaching materials and of appropriate pedagogical models. For many languages, online tools, courses, and digital archives have been developed in recent years that offer opportunities for both formal instruction and self-study. Now the…
Descriptors: Technology Integration, Second Language Instruction, Language Minorities, Artificial Intelligence
Shi, Zhan; Liu, Fengkai; Lai, Chun; Jin, Tan – Language Learning & Technology, 2022
Automated Writing Evaluation (AWE) systems have been found to enhance the accuracy, readability, and cohesion of writing responses (Stevenson & Phakiti, 2019). Previous research indicates that individual learners may have difficulty utilizing content-based AWE feedback and collaborative processing of feedback might help to cope with this…
Descriptors: Writing Instruction, Writing Evaluation, Feedback (Response), Accuracy
Godwin-Jones, Robert – Language Learning & Technology, 2023
Looking at human communication from the perspective of semiotics extends our view beyond verbal language to consider other sign systems and meaning-making resources. Those include gestures, body language, images, and sounds. From this perspective, the communicative process expands from individual mental processes of verbalizing to include features…
Descriptors: Second Language Learning, Second Language Instruction, Semiotics, Nonverbal Communication
David James Woo; Hengky Susanto; Chi Ho Yeung; Kai Guo; April Ka Yeng Fung – Language Learning & Technology, 2024
English as a foreign language (EFL) students' use of artificial intelligence (AI) tools that generate human-like text may enhance students' written work. However, the extent to which students use AI-generated text to complete a written composition and how AI-generated text influences the overall writing quality remain uncertain. 23 Hong Kong…
Descriptors: Artificial Intelligence, Writing Instruction, English Language Learners, English (Second Language)
Bakla, Arif – Language Learning & Technology, 2020
As digital technologies have become ubiquitous thanks to the Internet, new modes of feedback in L2 writing have emerged, yet what remains unclear is how feedback given through alternative modes helps improve writing quality and how new feedback tools fit in the overall context of writing instruction. Therefore, the purpose of this embedded…
Descriptors: Feedback (Response), English (Second Language), Computer Assisted Instruction, Writing Instruction